Traditional Muslim Social Workers in Secular Contexts
Bibliographic record
Abstract
In the current context of social work that advocates for inclusivity and diversity, amidst the growing traditional Muslim population in the Greater Toronto Area, the following qualitative study addresses a pressing subject and provides insights that can promote meaningful change. By engaging in six in-depth semi-structured interviews, the following papers address how religious, traditional Muslim social workers navigate and reconcile their traditional beliefs in a secularized social work context. The traditional Muslim has a complex relationship with Western models of social welfare, as evidenced by critiques from scholars such as Azmi (1991), Rasli (2022), and Ali (1989). These scholars highlight the traditional Muslim worldview is grounded in divine revelation and accordingly perceives social science frameworks through a unique lens, being built on Islamic principles. Moreover, an interpretive phenomenological approach (Beck, 2021) paired with Edward Said’s postcolonial theory (1978) within the enquiry revealed rich themes that delved deeper into underlying structural oppressions beyond the participant’s personal experience. These themes included: ideological conflicts, a hostile context, and coping strategies adopted by participants. Despite the challenging context and emotional strain, participants note the value of social work skills and how they reconcile their religious worldview with secular social work practices. The study exposes the need for Social Work education to overhaul its anti-Muslim biases and assumptions to accommodate diverse religious perspectives within educational settings and the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".